AutoML frameworks are getting better every day, and can provide high-performing ML pipelines, unique data insights, and ML explanations. No long

AutoML frameworks of the future - KDnuggets

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2021-05-17 07:51:39

AutoML frameworks are getting better every day, and can provide high-performing ML pipelines, unique data insights, and ML explanations. No longer black-boxes, these powerful tools offer self-documenting capabilities and native Python notebook support.

Automated Machine Learning (AutoML) is a process of building a complete Machine Learning pipeline automatically, without (or with minimal) human help. The AutoML solutions are quite new, with the first research papers from 2013 (Auto-Weka), 2015 (Auto-sklearn), and 2016 (TPOT). Currently, there are several AutoML open-source frameworks and commercial platforms available that can work with a variety of data. There is worth mentioning such open-source solutions like AutoGluon, H2O, or MLJAR AutoML.

The main goal of the AutoML framework was to find the best possible ML pipeline under the selected time budget. For its purpose, AutoML frameworks were training many different ML algorithms and tune their hyper-parameters. The improvements in the performance can be obtained by increasing the number of algorithms and checked hyper-parameters settings, which means longer computation time. But is the performance of the ML pipeline the only goal of the analysis?